data_dictionary: Health-related Quality of Life of Persons Living with Tuberculosis.sav
source: |
Tornu, Eric ; Quarcoopome, Louisa (2022), “Health-related Quality of Life of Persons Living with Tuberculosis”, Mendeley Data, V2, doi: 10.17632/jg4xp7883w.2
description: |
From the original source: This data is on a study which accessed the health-related quality of life of persons living with Tuberculosis.
Health-related quality of life, 2 of 6
Howwouldyourateyourqualityoflife_1:
values:
'1': Poor
'2': Neither poor nor good
'3': Good
'4': Very Good
HowSatisareyouwithyourhealth_2:
values:
'1': Very dissatisfied
'2': Dissatisfied
'3': Neither satisfied nor satisfied
'4': Satisfied
'5': Very Satisfied
Physicalpainprevents_3:
values:
'1': An extreme amount
'2': Very much
'3': A moderate amount
'4': A little
'5': Not at all
Health-related quality of life, 3 of 6
Howmuchmedicaltreatment_4:
values:
'1': An extreme amount
'2': Very much
'3': A moderate amount
'4': A little
'5': Not at all
Howmuchdoyouenjoylife_5:
values:
'1': Not at all
'2': A little
'3': A moderate amount
'4': Very much
'5': Extremely
Towhatextentdoyoufeelyourlifetobemeaningful_6:
values:
'1': Not at all
'2': A little
'3': A moderate amount
'4': Very much
'5': Extremely
Health-related quality of life, 4 of 6
Howwellareyouabletoconcentrate_7:
values:
'1': Not at all
'2': A little
'3': A moderate amount
'4': Very much
'5': Extremely
Howsafedoyoufeelinyourdailylife_8:
values:
'1': Not at all
'2': A little
'3': A moderate amount
'4': Very much
'5': Extremely
Howhealthyisyourphysicalenvironment_9:
values:
'1': Not at all
'2': A little
'3': A moderate amount
'4': Very much
'5': Extremely
Health-related quality of life, 5 of 6
Doyouhaveenoughenergyforeverydaylife_10:
values:
'1': Not at all
'2': A little
'3': A moderate amount
'4': Very much
'5': Extremely
Areyouabletoacceptyourbodilyappearance_11:
values:
'1': Not at all
'2': A little
'3': A moderate amount
'4': Very much
'5': Extremely
Haveyouenoughmoneytomeetyourneeds_12:
values:
'1': Not at all
'2': A little
'3': A moderate amount
'4': Very much
'5': Extremely
Health-related quality of life, 6 of 6
AvailableInformation_13:
values:
'1': Not at all
'2': A little
'3': A moderate amount
'4': Very much
'5': Extremely
Opportunityforleisureactivities_14:
values:
'1': Not at all
'2': A little
'3': A moderate amount
'4': Very much
'5': Extremely
Howwellareyouabletogetaround_15:
values:
'1': Very poor
'2': Poor
'3': Neither poor nor good
'4': Good
'5': Very Good
Correlation matrix, 1 of 3
Correlation matrix, 2 of 3
Correlation matrix, 3 of 3
Communalities
Eigenvalues
Scree plot
Component matrix
Live demo, Principal components analysis
Break #1
What you have learned
Principal components analysis
What’s coming next
Applications of principal components
Applications
Visualization
Reduce high dimensional visualization
Fewer graphs
Regression analysis
Fewer independent variables (rule of 15)
Removes collinearity
Boxplots of first four principal components
Scatterplot of first four principal components
R-squared using four principal components
R-squared using all 24 variables
Live demo, Applications of principal components
Break #2
What you have learned
Applications of principal components
What’s coming next
Factor analysis
Philosophy behind factor analysis
Variance equals information
Covariance (correlation) equals shared information
Modeling shared information creates latent variables
Factor rotation
Recombine factors
Strive for simple interpretation
Components close to -1, 0, or 1
Each variable has one and only one non-zero components
Not always achievable
Rotated factor pattern, 1 of 3
Rotated factor pattern, 2 of 3
Rotated factor pattern, 3 of 3
Live demo, Factor analysis
Break #3
What you have learned
Factor analysis
What’s coming next
Criticisms of principal components analysis and factor analysis
Criticisms of principal components analysis
Advantages
Makes collection of many variables manageable
Eliminates collinearity issues
Focus only on important sources of variation
Disadvantages
Components often uninterpretable
False sense of parsimony
Criticisms of factor analysis
Advantages
Explore underlying structure
Create or validate subscales
Disadvantages
Difficulty in choosing number of factors
Reification
Summary
What you have learned
Principal components analysis
Applications of principal components
Factor analysis
Criticisms of principal components analysis and factor analysis